Speaker Profile
M.D., Ph.D., Chief of Product and Chief Medical Officer,
Biography
Rahul Deo is a cardiologist and scientist with 15+ years of experience in academic medicine, most recently at the University of California, San Francisco, and Harvard Medical School, prior to co-founding Atman Health. He completed a PhD in Biophysics and postdoctoral training in Artificial Intelligence and trained in Internal Medicine at Brigham and Womens Hospital and Cardiology at Massachusetts General Hospital. His focus, both in academia and now at Atman Health, has been to bring technology to improve care, recognizing that a solution will require a deep understanding of both the biological complexity of the underlying diseases and the process of clinical management. He recognizes that the core of the problem is a fundamentally broken provider workflow that has resisted innovation, permitting marginal improvements, but no changes that will impact quality, access, or costs. He co-founded Atman Health to build a new model.
Talk
Artificial intelligence-powered specialty care for scaling, complexity, and quality.
Despite significant investment, the same problems remain in healthcare: high costs, poor quality, and limited access. Our software, validated in high-risk populations, redefines the clinical process by using LLM-based clinical data ingestion to feed a transparent, deterministic decision-making engine. Downstream tasks are all automated, enabling outstanding outcomes in a fraction of the time.
AI and Data Sciences Showcase:
Atman Health
Atman Health’s mission is to transform specialty care through artificial intelligence-powered software that enables high-quality, high-complexity care in a fraction of the time.
Session Abstract – PMWC 2026 Silicon Valley
The PMWC 2026 AI Company Showcase will provide a 15-30 minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.
